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Wireless Sensor Networks (WSNs) are widely used in detecting, locating and tracking moving objects. The cheap, low-powered and energy-limited sensors that are set up in large areas may consume large portion of energy and disable the whole network. In this paper, a new energy-efficient method based on Distributed Incremental Gene Expression Programming is proposed to discover the moving patterns of...
Extremely small, very low cost and very low power yet smart tactical sensors are commonly proposed. We have investigated the design patterns and algorithms that are capable of on-sensor dimensionality reduction so forwarding information rather than the raw data across these tactical sensor networks where data flows are hampered by a disconnected, intermittent and low bandwidth (DIL) operational domain...
The algorithm for moving target tracking in clusters of sensor networks is presented. The aim of the proposed architecture is suitable for large-scale area tracking; the technique is based on received signal strength indication (RSSI) and time of arrival (TOA) measurements. Here we use the extended Kalman filter (EKF) to estimate the moving target's trajectory by TOA measurement. The handoff decision...
This work deals with the problem of target tracking in wireless sensor networks where the observed system is assumed to evolve according to a probabilistic state space model. We propose to improve the use of the variational filtering (VF) by quantizing the data collected by the sensors to higher levels respecting the tradeoff between the information relevance of sensor measurements and the energy...
The original CPA (closest point of approach) algorithm can localize and track moving targets within a wireless sensor network that has a specific node configuration with respect to the target trajectory. As a target moves through a large network of randomly deployed sensors, the configuration of the nodes triggered along the target trajectory may not meet this requirement and will not localize and...
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